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A New Benchmark and Algorithm for Clothes-Changing Video Person Re-Identification

IEEE Transactions on Information Forensics and Security 2025年 20卷 1993-2005页

作者： Wang, Likai Zhang, Xiangqun Han, Ruize Wei, Yanjie Wang, Song Feng, Wei Tianjin University College of Intelligence and Computing Tianjin300350 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518107 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Person re-identification (Re-ID) is a classical computer vision task and has significant applications for public security and information forensics. Recently, long-term Re-ID with clothes-changing has attracted increasing attention. However, existing methods mainly focus on image-based setting, where richer temporal information is overlooked. In this paper, we focus on the relatively new yet practical problem of Clothes-Changing Video-based Re-ID (CCVReID), which is less studied. First, given the dataset shortage, we build two new benchmark datasets for CCVReID problem, including a large-scale synthetic video dataset and a real-world one, both containing human sequences with various clothing changes. Moreover, we systematically study this problem by simultaneously considering the classical appearance feature and temporal feature contained in the video. We develop a dual-branch fusion framework that makes use of the information from both clothes-aware appearance feature and clothes-free gait feature. For better information fusion, a confidence-guided re-ranking strategy is proposed to adaptively balance the weight of these two categories of features. We have released the benchmark and code proposed in this work to the public at https://***/kkw98/CCVReID. © 2025 IEEE.

关键词： Reamers

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System Optimal Routing and Distribution of Benefits on National Road Networks

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Transportation Research Procedia 2025年 82卷 2532-2545页

作者： Alexander Roocroft Giuliano Punzo Muhamad Azfar Ramli Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Institute of High Performance Computing A*STAR Singapore

Rerouting drivers from selfish route choices to system-optimal traffic patterns has the potential to improve the performance of existing infrastructure. Previous research has looked into assessing the potential of rerouting through the empirical price of anarchy, a measure of network efficiency. However, studies using real-world measurements have been limited by methodological accuracy and network size. Also, they have lacked understanding of the spatial distribution of benefits from rerouting and the relationship with marginal external cost road charges that can be used for implementation. In this article, we create an accurate data-driven traffic assignment model of England's Strategic Road Network. We use it to calculate the national price of anarchy, which is found to be almost 1 implying gains from rerouting at the national scale are minimal and smaller than in other studies. The results show the distribution of rerouting benefits varies strongly with different network zones and demand profiles. This did not match the distribution of marginal external cost charges. Some zones have noticeable benefits from rerouting although the overall network benefit is small, however, these zones do not coincide with where the largest road charges have to be applied for system-optimal rerouting. These results have implications for rerouting implementation.

关键词： Strategic Road Network Empirical Price of Anarchy National Traffic Assignment

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Impact of climate and Aedes albopictus establishment on dengue and chikungunya outbreaks in Europe: a time-to-event analysis

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The Lancet Planetary Health 2025年第5期9卷 e374-e383页

作者： Farooq, Zia Segelmark, Leo Rocklöv, Joacim Lillepold, Kate Sewe, Maquines Odhiambo Briet, Olivier J T Semenza, Jan C Department of Epidemiology and Global Health Umeå University Umeå Sweden Heidelberg Institute of Global Health University of Heidelberg Heidelberg Germany Interdisciplinary Center for Scientific Computing University of Heidelberg Heidelberg Germany ECDC Traineeship Programme European Centre for Disease Prevention and Control Solna Sweden European Centre for Disease Prevention and Control (ECDC) Solna Sweden

Background: The rapid spread of the Asian tiger mosquito (Aedes albopictus) poses a notable public health threat in Europe due to its ability to transmit tropical diseases such as dengue and chikungunya. We aimed to quantify the underlying drivers facilitating and accelerating Europe's transition from sporadic arbovirus outbreaks to Aedes-borne disease endemicity, focusing on dengue and chikungunya outbreaks. Methods: We conducted a time-to-event analysis to investigate the period between establishment of Ae albopictus and autochthonous dengue and chikungunya outbreaks across Nomenclature of Territorial Units for Statistics (NUTS) 3 regions in the EU. We incorporated data from the European Centre for Disease Prevention and control, WHO, technical and surveillance reports, and other entomological data sources on regional Ae albopictus establishment and subsequent dengue and chikungunya outbreaks from 1990 (when Ae albopictus was first introduced to an EU country) to 2024. The main outcome was survival time (ie, the time from Ae albopictus establishment to an outbreak of dengue or chikungunya), accounting for land-use types, demographic and socioeconomic factors, imported cases, and climatic variables via univariable and multivariable regression. To address recurrent outbreaks, we applied the Andersen–Gill extension of the Cox proportional hazards model to analyse all events. We further stratified regions into warm and cool groups on the basis of mean summer temperatures above or below 20°C and conducted a stratified analysis with Kaplan–Meier curves and the log-rank test to evaluate differences between these groups. We also estimated projected outbreak hazards from the 2030s to the 2060s at a decadal scale under three distinct shared socioeconomic pathways (SSPs;SSP1–2·6, SSP3–7·0, and SSP5–8·5) to assess the future impact of climate change on outbreak hazard estimates. Findings: Between 1990 and 2024, the interval from the first NUTS 3 regional establishment of Ae a

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Who Should I Trust? A Visual Analytics Approach for Comparing Net Load Forecasting Models

Who Should I Trust? A Visual Analytics Approach for Comparin...

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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Bhattacharjee, Kaustav Kundu, Soumya Chakraborty, Indrasis Dasgupta, Aritra New Jersey Institute of Technology Department of Data Science United States Pacific Northwest National Laboratory Optimization and Control Group United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States

ISBN: (纸本)9798350352528

Net load forecasting is crucial for energy planning and facilitating informed decision-making regarding trade and load distributions. However, evaluating forecasting models' performance against benchmark models remains challenging, thereby impeding experts' trust in the model's performance. In this context, there is a demand for technological interventions that allow scientists to compare models across various timeframes and solar penetration levels. This paper introduces a visual analytics-based application designed to compare the performance of deep-learning-based net load forecasting models with other models for probabilistic net load forecasting. This application employs carefully selected visual analytic interventions, enabling users to discern differences in model performance across different solar penetration levels, dataset resolutions, and hours of the day over multiple months. We also present observations made using our application through a case study, demonstrating the effectiveness of visualizations in aiding scientists in making informed decisions and enhancing trust in net load forecasting models. © 2025 IEEE.

关键词： AI/ML net load forecasting visual analytics

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MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN 2

MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Krishna, Tiruveedula Gopi Sreenivas, Nune Ajay Kumar, M. Reddy, Chandra Sekhar Rathee, Davinder Sing Urgessa, Teklu College of Electrical Engineering and Computing Adama Science and Technology University Department of Computer Science and Engineering Adama Ethiopia Andhra Loyala institute of Engineering and Technology Department of Electrical and Electronics Engineering Vijayawada India College of Electrical Engineering and Computing Adama Science and Technology University Department of Electrical Power and Control Engineering Adama Ethiopia College of Electrical Engineering and Computing Adama Science and Technology University Department of ECE Adama Ethiopia

ISBN: (纸本)9798331505745

The research presents a new efficient machine learning method to classify brain tumors because this task remains vital in fighting the high incidence of brain cancers. The proposed approach unites all its operations including image segmentation with feature extraction and classification into a single processing system. This study uses Particle Swarm Optimization (PSO) and Adaptive PSO (APSO) methodologies to optimize Locally Linear Radial Basis Function Neural Network (LLRBFNN) resulting in PSO-LLRBFNN and APSO-LLRBFNN model implementations. The research examined the optimized APSO-LLRBFNN models against Support Vector Machines (SVM) and Least Mean Squares (LMS) based LLRBFNN classifier methods. Results from experiments show that APSO-LLRBFNN gives superior outcomes with 98.93% accuracy and takes 12.23 seconds for execution, which surpasses currently available methods. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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controller Synthesis of Collaborative Signal Temporal Logic Tasks for Multi-Agent Systems via Assume-Guarantee Contracts

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IEEE Transactions on Automatic control 2025年

作者： Liu, Siyuan Saoud, Adnane Dimarogonas, Dimos V. KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden University Mohammed VI Polytechnic College of Computing Benguerir Morocco

This paper considers the problem of controller synthesis of a fragment of signal temporal logic (STL) specifications for large-scale multi-agent systems, where the agents are dynamically coupled and subject to collaborative tasks. A compositional framework based on continuous-time assume-guarantee contracts is developed to break the complex and large synthesis problem into subproblems of manageable sizes. We first show how to formulate the collaborative STL tasks as assume-guarantee contracts by leveraging the idea of prescribed performance control. The concept of contracts is used to establish our compositionality result, which allows us to guarantee the satisfaction of a global contract by the multi-agent system when all agents satisfy their local contracts. Then, a closed-form continuous-time feedback controller is designed to enforce local contracts over the agents in a distributed manner, which further guarantees global task satisfaction based on the compositionality result. The effectiveness of our results is demonstrated by two numerical examples. © 1963-2012 IEEE.

关键词： Continuous time systems

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Observer Based Fast Fixed-Time Non-Singular Terminal Sliding Mode control of Teleoperation Systems

Observer Based Fast Fixed-Time Non-Singular Terminal Sliding...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Neila Mezghani Ben Romdhane Moncef Triki Sofiene Hajji Tarak Damak Laboratory of Sciences and Techniques of Automatic control and computing (Lab-STA) Higher Institute of Industriels Systems of Gabes Gabes Tunisia Laboratory of Sciences and Techniques of Automatic control and computing (Lab-STA) National School of Electronics and Telecommunications of Sfax Sfax Tunisia Laboratory of Sciences and Techniques of Automatic control and computing (Lab STA) National School of Engineering of Sfax Sfax Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This paper introduces a fast fixed-time non-singular terminal sliding mode control approach, utilizing a sliding mode observer, to tackle the tracking challenge in teleoperation systems. A robust fixed-time control law is formulated to achieve accurate tracking performance, effectively countering parametric uncertainties, external disturbances, and time-varying delays. Furthermore, the proposed fast fixed-time non-singular terminal sliding surface ensures convergence within a fixed time frame once the sliding condition is satisfied. To improve practical implementation, a sliding mode observer is integrated, removing the necessity for a velocity sensor. The system's stability is demonstrated through Lyapunov theory.

关键词： Uncertainty Simulation Observers Robot sensing systems Main-secondary Stability analysis Delays Sliding mode control Time-varying systems Convergence

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Optimizing Human Activity Recognition Using Stacked CNN with Ensemble Learning

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Procedia Computer Science 2025年 258卷 1598-1607页

作者： Divya Yadav Deepika Rani Om Parakash Verma Department of Mathematics and Computing Dr B R Ambedkar National Institute of Technology Jalandhar Jalandhar Punjab India Department of Instrumentation and Control Engineering Dr B R Ambedkar National Institute of Technology Jalandhar Jalandhar Punjab India

In recent times, Human Activity Recognition (HAR) plays a crucial role in numerous artificial intelligence applications, including surveillance, healthcare, and security. However, the primary challenges in accurately identifying human activities from video data include occlusion, low resolution, data scarcity, and high computational costs. The main objective of this study is to enhance both the recognition accuracy and computational efficiency. This study presents a computationally efficient HAR model that leverages deep learning and machine learning algorithms for feature extraction and classification tasks. The proposed model consists of three fine-tuned convolution neural networks: VGG16, VGG19, and MobileNetV2 for feature extraction, combined with four classifiers, Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), and Fuzzy classifier. The performance of the proposed model was evaluated using three benchmark datasets: KTH, YouTube11, and Peliculas. The proposed method demonstrated exceptional performance of 99.60% and 99.50% accuracy on the KTH and Peliculas dataset using MobileNet and SVM. Additionally, the proposed method achieved a superior performance of 99.80% on the YouTube11 dataset with a combination of VGG16 and SVM. Finally, the performance of the proposed model was compared and validated against previous models.

关键词： Human Activity Recognition (HAR) Convolution Neural Network (CNN) Feature Extraction Classification

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Interaction-Aware Gaussian Weighting for Clustered Federated Learning

arXiv

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arXiv 2025年

作者： Licciardi, Alessandro Leo, Davide Faní, Eros Caputo, Barbara Ciccone, Marco Department of Mathematical Sciences Polytechnic University of Turin Italy Turin Italy Department of Computing and Control Engineering Polytechnic University of Turin Italy Vector Institute TorontoON Canada

Federated Learning (FL) emerged as a decentralized paradigm to train models while preserving privacy. However, conventional FL struggles with data heterogeneity and class imbalance, which degrade model performance. Clustered FL balances personalization and decentralized training by grouping clients with analogous data distributions, enabling improved accuracy while adhering to privacy constraints. This approach effectively mitigates the adverse impact of heterogeneity in FL. In this work, we propose a novel clustered FL method, FedGWC (Federated Gaussian Weighting Clustering), which groups clients based on their data distribution, allowing training of a more robust and personalized model on the identified clusters. FedGWC identifies homogeneous clusters by transforming individual empirical losses to model client interactions with a Gaussian reward mechanism. Additionally, we introduce the Wasserstein Adjusted Score, a new clustering metric for FL to evaluate cluster cohesion with respect to the individual class distribution. Our experiments on benchmark datasets show that FedGWC outperforms existing FL algorithms in cluster quality and classification accuracy, validating the efficacy of our approach. © 2025, CC BY.

关键词： Federated learning

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